@spec calcError(t(), Evision.ML.TrainData.t(), boolean()) ::
  {number(), Evision.Mat.t()} | {:error, String.t()}

Computes error on the training or test dataset

Positional Arguments

• self: Evision.ML.ANNMLP.t()

• data: Evision.ML.TrainData.t().

  the training data

• test: bool.

  if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing.

Return

• retval: float

• resp: Evision.Mat.t().

  the optional output responses.

The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).

Python prototype (for reference only):

calcError(data, test[, resp]) -> retval, resp

@spec calcError(
  t(),
  Evision.ML.TrainData.t(),
  boolean(),
  [{atom(), term()}, ...] | nil
) ::
  {number(), Evision.Mat.t()} | {:error, String.t()}

Computes error on the training or test dataset

Positional Arguments

• self: Evision.ML.ANNMLP.t()

• data: Evision.ML.TrainData.t().

  the training data

• test: bool.

  if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing.

Return

• retval: float

• resp: Evision.Mat.t().

  the optional output responses.

The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).

Python prototype (for reference only):

calcError(data, test[, resp]) -> retval, resp

@spec clear(t()) :: t() | {:error, String.t()}

Clears the algorithm state

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Python prototype (for reference only):

clear() -> None

@spec create() :: t() | {:error, String.t()}

Creates empty model

Return

• retval: Evision.ML.ANNMLP.t()

Use StatModel::train to train the model, Algorithm::load\<ANN_MLP>(filename) to load the pre-trained model. Note that the train method has optional flags: ANN_MLP::TrainFlags.

Python prototype (for reference only):

create() -> retval

@spec empty(t()) :: boolean() | {:error, String.t()}

empty

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: bool

Python prototype (for reference only):

empty() -> retval

@spec getAnnealCoolingRatio(t()) :: number() | {:error, String.t()}

getAnnealCoolingRatio

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: double

@see setAnnealCoolingRatio/2

Python prototype (for reference only):

getAnnealCoolingRatio() -> retval

@spec getAnnealFinalT(t()) :: number() | {:error, String.t()}

getAnnealFinalT

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: double

@see setAnnealFinalT/2

Python prototype (for reference only):

getAnnealFinalT() -> retval

@spec getAnnealInitialT(t()) :: number() | {:error, String.t()}

getAnnealInitialT

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: double

@see setAnnealInitialT/2

Python prototype (for reference only):

getAnnealInitialT() -> retval

@spec getAnnealItePerStep(t()) :: integer() | {:error, String.t()}

getAnnealItePerStep

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: int

@see setAnnealItePerStep/2

Python prototype (for reference only):

getAnnealItePerStep() -> retval

@spec getBackpropMomentumScale(t()) :: number() | {:error, String.t()}

getBackpropMomentumScale

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: double

@see setBackpropMomentumScale/2

Python prototype (for reference only):

getBackpropMomentumScale() -> retval

@spec getBackpropWeightScale(t()) :: number() | {:error, String.t()}

getBackpropWeightScale

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: double

@see setBackpropWeightScale/2

Python prototype (for reference only):

getBackpropWeightScale() -> retval

@spec getDefaultName(t()) :: binary() | {:error, String.t()}

getDefaultName

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: String

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Python prototype (for reference only):

getDefaultName() -> retval

@spec getLayerSizes(t()) :: Evision.Mat.t() | {:error, String.t()}

getLayerSizes

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: Evision.Mat.t()

Integer vector specifying the number of neurons in each layer including the input and output layers. The very first element specifies the number of elements in the input layer. The last element - number of elements in the output layer. @sa setLayerSizes

Python prototype (for reference only):

getLayerSizes() -> retval

@spec getRpropDW0(t()) :: number() | {:error, String.t()}

getRpropDW0

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: double

@see setRpropDW0/2

Python prototype (for reference only):

getRpropDW0() -> retval

@spec getRpropDWMax(t()) :: number() | {:error, String.t()}

getRpropDWMax

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: double

@see setRpropDWMax/2

Python prototype (for reference only):

getRpropDWMax() -> retval

@spec getRpropDWMin(t()) :: number() | {:error, String.t()}

getRpropDWMin

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: double

@see setRpropDWMin/2

Python prototype (for reference only):

getRpropDWMin() -> retval

@spec getRpropDWMinus(t()) :: number() | {:error, String.t()}

getRpropDWMinus

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: double

@see setRpropDWMinus/2

Python prototype (for reference only):

getRpropDWMinus() -> retval

@spec getRpropDWPlus(t()) :: number() | {:error, String.t()}

getRpropDWPlus

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: double

@see setRpropDWPlus/2

Python prototype (for reference only):

getRpropDWPlus() -> retval

@spec getTermCriteria(t()) :: {integer(), integer(), number()} | {:error, String.t()}

getTermCriteria

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: TermCriteria

@see setTermCriteria/2

Python prototype (for reference only):

getTermCriteria() -> retval

@spec getTrainMethod(t()) :: integer() | {:error, String.t()}

getTrainMethod

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: int

Returns current training method

Python prototype (for reference only):

getTrainMethod() -> retval

@spec getVarCount(t()) :: integer() | {:error, String.t()}

Returns the number of variables in training samples

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: int

Python prototype (for reference only):

getVarCount() -> retval

@spec getWeights(t(), integer()) :: Evision.Mat.t() | {:error, String.t()}

getWeights

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• layerIdx: int

Return

• retval: Evision.Mat.t()

Python prototype (for reference only):

getWeights(layerIdx) -> retval

@spec isClassifier(t()) :: boolean() | {:error, String.t()}

Returns true if the model is classifier

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: bool

Python prototype (for reference only):

isClassifier() -> retval

@spec isTrained(t()) :: boolean() | {:error, String.t()}

Returns true if the model is trained

Positional Arguments

• self: Evision.ML.ANNMLP.t()

Return

• retval: bool

Python prototype (for reference only):

isTrained() -> retval

@spec load(binary()) :: t() | {:error, String.t()}

Loads and creates a serialized ANN from a file

Positional Arguments

• filepath: String.

  path to serialized ANN

Return

• retval: Evision.ML.ANNMLP.t()

Use ANN::save to serialize and store an ANN to disk. Load the ANN from this file again, by calling this function with the path to the file.

Python prototype (for reference only):

load(filepath) -> retval

@spec predict(t(), Evision.Mat.maybe_mat_in()) ::
  {number(), Evision.Mat.t()} | {:error, String.t()}

Predicts response(s) for the provided sample(s)

Positional Arguments

• self: Evision.ML.ANNMLP.t()

• samples: Evision.Mat.t().

  The input samples, floating-point matrix

Keyword Arguments

• flags: int.

  The optional flags, model-dependent. See cv::ml::StatModel::Flags.

Return

• retval: float

• results: Evision.Mat.t().

  The optional output matrix of results.

Python prototype (for reference only):

predict(samples[, results[, flags]]) -> retval, results

@spec predict(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) ::
  {number(), Evision.Mat.t()} | {:error, String.t()}

Predicts response(s) for the provided sample(s)

Positional Arguments

• self: Evision.ML.ANNMLP.t()

• samples: Evision.Mat.t().

  The input samples, floating-point matrix

Keyword Arguments

• flags: int.

  The optional flags, model-dependent. See cv::ml::StatModel::Flags.

Return

• retval: float

• results: Evision.Mat.t().

  The optional output matrix of results.

Python prototype (for reference only):

predict(samples[, results[, flags]]) -> retval, results

@spec read(t(), Evision.FileNode.t()) :: t() | {:error, String.t()}

Reads algorithm parameters from a file storage

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• fn_: Evision.FileNode.t()

Python prototype (for reference only):

read(fn_) -> None

@spec save(t(), binary()) :: t() | {:error, String.t()}

save

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• filename: String

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

Python prototype (for reference only):

save(filename) -> None

@spec setActivationFunction(t(), integer()) :: t() | {:error, String.t()}

setActivationFunction

Positional Arguments

• self: Evision.ML.ANNMLP.t()

• type: int.

  The type of activation function. See ANN_MLP::ActivationFunctions.

Keyword Arguments

• param1: double.

  The first parameter of the activation function, \f$\alpha\f$. Default value is 0.

• param2: double.

  The second parameter of the activation function, \f$\beta\f$. Default value is 0.

Initialize the activation function for each neuron. Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.

Python prototype (for reference only):

setActivationFunction(type[, param1[, param2]]) -> None

@spec setActivationFunction(t(), integer(), [{atom(), term()}, ...] | nil) ::
  t() | {:error, String.t()}

setActivationFunction

Positional Arguments

• self: Evision.ML.ANNMLP.t()

• type: int.

  The type of activation function. See ANN_MLP::ActivationFunctions.

Keyword Arguments

• param1: double.

  The first parameter of the activation function, \f$\alpha\f$. Default value is 0.

• param2: double.

  The second parameter of the activation function, \f$\beta\f$. Default value is 0.

Initialize the activation function for each neuron. Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.

Python prototype (for reference only):

setActivationFunction(type[, param1[, param2]]) -> None

@spec setAnnealCoolingRatio(t(), number()) :: t() | {:error, String.t()}

setAnnealCoolingRatio

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• val: double

@see getAnnealCoolingRatio/1

Python prototype (for reference only):

setAnnealCoolingRatio(val) -> None

@spec setAnnealFinalT(t(), number()) :: t() | {:error, String.t()}

setAnnealFinalT

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• val: double

@see getAnnealFinalT/1

Python prototype (for reference only):

setAnnealFinalT(val) -> None

@spec setAnnealInitialT(t(), number()) :: t() | {:error, String.t()}

setAnnealInitialT

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• val: double

@see getAnnealInitialT/1

Python prototype (for reference only):

setAnnealInitialT(val) -> None

@spec setAnnealItePerStep(t(), integer()) :: t() | {:error, String.t()}

setAnnealItePerStep

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• val: int

@see getAnnealItePerStep/1

Python prototype (for reference only):

setAnnealItePerStep(val) -> None

@spec setBackpropMomentumScale(t(), number()) :: t() | {:error, String.t()}

setBackpropMomentumScale

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• val: double

@see getBackpropMomentumScale/1

Python prototype (for reference only):

setBackpropMomentumScale(val) -> None

@spec setBackpropWeightScale(t(), number()) :: t() | {:error, String.t()}

setBackpropWeightScale

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• val: double

@see getBackpropWeightScale/1

Python prototype (for reference only):

setBackpropWeightScale(val) -> None

@spec setLayerSizes(t(), Evision.Mat.maybe_mat_in()) :: t() | {:error, String.t()}

setLayerSizes

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• layer_sizes: Evision.Mat.t()

Integer vector specifying the number of neurons in each layer including the input and output layers. The very first element specifies the number of elements in the input layer. The last element - number of elements in the output layer. Default value is empty Mat. @sa getLayerSizes

Python prototype (for reference only):

setLayerSizes(_layer_sizes) -> None

@spec setRpropDW0(t(), number()) :: t() | {:error, String.t()}

setRpropDW0

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• val: double

@see getRpropDW0/1

Python prototype (for reference only):

setRpropDW0(val) -> None

@spec setRpropDWMax(t(), number()) :: t() | {:error, String.t()}

setRpropDWMax

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• val: double

@see getRpropDWMax/1

Python prototype (for reference only):

setRpropDWMax(val) -> None

@spec setRpropDWMin(t(), number()) :: t() | {:error, String.t()}

setRpropDWMin

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• val: double

@see getRpropDWMin/1

Python prototype (for reference only):

setRpropDWMin(val) -> None

@spec setRpropDWMinus(t(), number()) :: t() | {:error, String.t()}

setRpropDWMinus

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• val: double

@see getRpropDWMinus/1

Python prototype (for reference only):

setRpropDWMinus(val) -> None

@spec setRpropDWPlus(t(), number()) :: t() | {:error, String.t()}

setRpropDWPlus

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• val: double

@see getRpropDWPlus/1

Python prototype (for reference only):

setRpropDWPlus(val) -> None

@spec setTermCriteria(t(), {integer(), integer(), number()}) ::
  t() | {:error, String.t()}

setTermCriteria

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• val: TermCriteria

@see getTermCriteria/1

Python prototype (for reference only):

setTermCriteria(val) -> None

@spec setTrainMethod(t(), integer()) :: t() | {:error, String.t()}

setTrainMethod

Positional Arguments

• self: Evision.ML.ANNMLP.t()

• method: int.

  Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.

Keyword Arguments

• param1: double.

  passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP and to initialT for ANN_MLP::ANNEAL.

• param2: double.

  passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP and to finalT for ANN_MLP::ANNEAL.

Sets training method and common parameters.

Python prototype (for reference only):

setTrainMethod(method[, param1[, param2]]) -> None

@spec setTrainMethod(t(), integer(), [{atom(), term()}, ...] | nil) ::
  t() | {:error, String.t()}

setTrainMethod

Positional Arguments

• self: Evision.ML.ANNMLP.t()

• method: int.

  Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.

Keyword Arguments

• param1: double.

  passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP and to initialT for ANN_MLP::ANNEAL.

• param2: double.

  passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP and to finalT for ANN_MLP::ANNEAL.

Sets training method and common parameters.

Python prototype (for reference only):

setTrainMethod(method[, param1[, param2]]) -> None

@spec train(t(), Evision.ML.TrainData.t()) :: boolean() | {:error, String.t()}

Trains the statistical model

Positional Arguments

• self: Evision.ML.ANNMLP.t()

• trainData: Evision.ML.TrainData.t().

  training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.

Keyword Arguments

• flags: int.

  optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).

Return

• retval: bool

Python prototype (for reference only):

train(trainData[, flags]) -> retval

@spec train(t(), Evision.ML.TrainData.t(), [{atom(), term()}, ...] | nil) ::
  boolean() | {:error, String.t()}

Trains the statistical model

Positional Arguments

• self: Evision.ML.ANNMLP.t()

• trainData: Evision.ML.TrainData.t().

  training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.

Keyword Arguments

• flags: int.

  optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).

Return

• retval: bool

Python prototype (for reference only):

train(trainData[, flags]) -> retval

@spec train(t(), Evision.Mat.maybe_mat_in(), integer(), Evision.Mat.maybe_mat_in()) ::
  boolean() | {:error, String.t()}

Trains the statistical model

Positional Arguments

• self: Evision.ML.ANNMLP.t()

• samples: Evision.Mat.t().

  training samples

• layout: int.

  See ml::SampleTypes.

• responses: Evision.Mat.t().

  vector of responses associated with the training samples.

Return

• retval: bool

Python prototype (for reference only):

train(samples, layout, responses) -> retval

@spec write(t(), Evision.FileStorage.t()) :: t() | {:error, String.t()}

Stores algorithm parameters in a file storage

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• fs: Evision.FileStorage.t()

Python prototype (for reference only):

write(fs) -> None

@spec write(t(), Evision.FileStorage.t(), binary()) :: t() | {:error, String.t()}

write

Positional Arguments

• self: Evision.ML.ANNMLP.t()
• fs: Evision.FileStorage.t()
• name: String

Has overloading in C++

Python prototype (for reference only):

write(fs, name) -> None